US Renewable Natural Gas Analysis Report
Kweku Reginald Wade
04/28/2022
Introduction
In the last three years there has been a seismic shift in what investors value. No longer are they looking to corporations for merely a return on investment, but a return on social capital. Social Capital has been defined in many ways by many people, but I define social capital in terms how corporations treat the Environment, Society, and how they Govern their employees.Taking the first initial from the words: Environment, Society, and Governance, yields the acronym ESG. ESG has become the de-facto measure investors use to grade corporations’ social capital.
In terms of the E in ESG, investors are looking to invest in renewable projects. Once such project type is Renewable Natural Gas. Renewable Natural Gas is natural gas that comes from non-fossil sources, such as landfills and farms. Landfills can products methane, the key component in natural gas, that is released by means of decaying bio-material found in trash. Farm animals also release methane through bodily waste that can be captured in an anaerobic digestion system.
My report seeks to answer four questions for investors:
- What is the future outlook on RNG?
- What states are leading the charge?
- What is the demand for RNG?
- Does it make a difference on the environment?
Required Packages
The following libraries were essential in order to complete my project:
- tidyverse - Used for reading, formatting, and plotting data
- readxl
- dplyr
- magrittr
- ggplot2
- janitor - Used for cleaning data
- data.table - Used for formatting data tables
- formattable - Used for formatting data tables
- plotly - Used for plotting data
Source Data
The source of my data originated from the Environmental Protection Agency, better known by it’s acronym, the EPA. The EPA provides a wealth of data on renewable natural gas projects in both the Landfill and the farm animal flavor. The farm animal projects are better known as AgStar projects.
The landfill data will be loaded from the LFG Energy Project Data Files as of March 2022. The location of this file can be located from the following URL: Source: https://www.epa.gov/lmop/lmop-landfill-and-project-database
#Loading in landfill project data
landfillgas_raw = read_excel("Data/lmopdata.xlsx", sheet="LMOP Database")The AgSTAR Livestock Anaerobic Digester Database can be loaded from the following URL: https://www.epa.gov/agstar/livestock-anaerobic-digester-database
AgSTAR publishes updated data on this website periodically (generally, two times per year) to enhance public access to information and support the development of biogas recovery projects
#Loading AgStar project data
anaerobicdigesterfas_raw = read_excel("Data/agstar-livestock-ad-database.xlsx", sheet="Operational and Construction")The Renewable Natural Gas, or RNG, facility data contains details of 173 facilities around the United States.
#Loading RNG Facility data
rng_facility_raw = read_csv("Data/RNG_Facilities.csv", show_col_types = FALSE)Data Summary
The data describes the physical and geo location of landfill in the United States. The data also contains project information such as when the project started, the amount of wastes in tons the landfill holds, the amount of gas and, or amount of electricity that the landfill produces as well as emission reduction and avoidance data. The Landfill data consists of the following schema:
#Show names of the columns
names(landfillgas_raw)## [1] "GHGRP ID"
## [2] "Landfill ID"
## [3] "Landfill Name"
## [4] "State"
## [5] "Physical Address"
## [6] "City"
## [7] "County"
## [8] "Zip Code"
## [9] "Latitude"
## [10] "Longitude"
## [11] "Ownership Type"
## [12] "Landfill Owner Organization(s)"
## [13] "Year Landfill Opened"
## [14] "Landfill Closure Year"
## [15] "Current Landfill Status"
## [16] "Waste in Place (tons)"
## [17] "Waste in Place Year"
## [18] "LFG Collection System In Place?"
## [19] "LFG Collected (mmscfd)"
## [20] "LFG Flared (mmscfd)"
## [21] "Project ID"
## [22] "Current Project Status"
## [23] "Project Name"
## [24] "Project Start Date"
## [25] "Project Shutdown Date"
## [26] "Project Type Category"
## [27] "LFG Energy Project Type"
## [28] "RNG Delivery Method"
## [29] "Actual MW Generation"
## [30] "Rated MW Capacity"
## [31] "LFG Flow to Project (mmscfd)"
## [32] "Current Year Emission Reductions (MMTCO2e/yr) - Direct"
## [33] "Current Year Emission Reductions (MMTCO2e/yr) - Avoided"The data describes the physical location of Anaerobic digesters in the United States. The data also contains project information such as when the farm become operational, the type of animal/farm, the amount of gas and, or amount of electricity that the farm produces as well as emission reduction data. The AgSTAR Anaerobic Digester data consists of the following schema:
#Show names of the columns
names(anaerobicdigesterfas_raw)## [1] "Project Name"
## [2] "Project Type"
## [3] "City"
## [4] "County"
## [5] "State"
## [6] "Digester Type"
## [7] "Status"
## [8] "Year Operational"
## [9] "Animal/Farm Type(s)"
## [10] "Cattle"
## [11] "Dairy"
## [12] "Poultry"
## [13] "Swine"
## [14] "Co-Digestion"
## [15] "Biogas Generation Estimate (cu_ft/day)"
## [16] "Electricity Generated (kWh/yr)"
## [17] "Biogas End Use(s)"
## [18] "System Designer(s)_Developer(s) and Affiliates"
## [19] "Receiving Utility"
## [20] "Total Emission Reductions (MTCO2e/yr)"
## [21] "Awarded USDA Funding?"The data describes the physical and geo-location of RNG facilities in the United States. The data also contains the amount of gas the facility produces.
The RNG facility data consists of the following schema:
#Show names of the columns
names(rng_facility_raw)## [1] "Facility Name" "Category"
## [3] "Type" "City"
## [5] "State" "Start date"
## [7] "Gas Flow to project (scfm)" "Source"
## [9] "Date updated" "Latitude"
## [11] "Longitude" "x"
## [13] "y"Data Cleaning
As you have seen from the previous schema, many of the column names consist of spaces. We now use the janitor library to transform the columns names so that any special character will be replaces by an underscore.
The following removes special characters from the Landfill columns and replaces them with underscores.
#Clean landfill data columns
landfillgas_clean = clean_names(landfillgas_raw)
names(landfillgas_clean)## [1] "ghgrp_id"
## [2] "landfill_id"
## [3] "landfill_name"
## [4] "state"
## [5] "physical_address"
## [6] "city"
## [7] "county"
## [8] "zip_code"
## [9] "latitude"
## [10] "longitude"
## [11] "ownership_type"
## [12] "landfill_owner_organization_s"
## [13] "year_landfill_opened"
## [14] "landfill_closure_year"
## [15] "current_landfill_status"
## [16] "waste_in_place_tons"
## [17] "waste_in_place_year"
## [18] "lfg_collection_system_in_place"
## [19] "lfg_collected_mmscfd"
## [20] "lfg_flared_mmscfd"
## [21] "project_id"
## [22] "current_project_status"
## [23] "project_name"
## [24] "project_start_date"
## [25] "project_shutdown_date"
## [26] "project_type_category"
## [27] "lfg_energy_project_type"
## [28] "rng_delivery_method"
## [29] "actual_mw_generation"
## [30] "rated_mw_capacity"
## [31] "lfg_flow_to_project_mmscfd"
## [32] "current_year_emission_reductions_mmtco2e_yr_direct"
## [33] "current_year_emission_reductions_mmtco2e_yr_avoided"The following removes special characters from the AgSTAR Anaerobic Digester Data columns and replaces them with underscores.
#Clean AgStart Anaerobic Digester data columns
anaerobicdigesterfas_clean = clean_names(anaerobicdigesterfas_raw)
names(anaerobicdigesterfas_clean)## [1] "project_name"
## [2] "project_type"
## [3] "city"
## [4] "county"
## [5] "state"
## [6] "digester_type"
## [7] "status"
## [8] "year_operational"
## [9] "animal_farm_type_s"
## [10] "cattle"
## [11] "dairy"
## [12] "poultry"
## [13] "swine"
## [14] "co_digestion"
## [15] "biogas_generation_estimate_cu_ft_day"
## [16] "electricity_generated_k_wh_yr"
## [17] "biogas_end_use_s"
## [18] "system_designer_s_developer_s_and_affiliates"
## [19] "receiving_utility"
## [20] "total_emission_reductions_mtco2e_yr"
## [21] "awarded_usda_funding"The following removes special characters from the RNG facility data columns and replaces them with underscores.
#Clean RNG Facility data columns
rng_facility_clean = clean_names(rng_facility_raw)
names(rng_facility_clean)## [1] "facility_name" "category"
## [3] "type" "city"
## [5] "state" "start_date"
## [7] "gas_flow_to_project_scfm" "source"
## [9] "date_updated" "latitude"
## [11] "longitude" "x"
## [13] "y"EDA
In our first step at exploratory data analysis we look at the first 5 rows of the Landfill. A few columns of note is the project name of the landfill, its location, the amount of waste, the project type and the start date of the project.
| landfill_name | project_name | state | waste_in_place_tons | lfg_energy_project_type | project_start_date |
|---|---|---|---|---|---|
| Anchorage Regional Landfill | Project #1 | AK | 10400368 | Reciprocating Engine | 2012-07-31 |
| Anchorage Regional Landfill | Project #1, Expansion #1 | AK | 10400368 | Reciprocating Engine | 2014-01-01 |
| Capitol Disposal Landfill | NA | AK | 950804 | Unknown | NA |
| Central Peninsula Landfill (CPL) | Project #1 | AK | 1721799 | Cogeneration | 2023-01-01 |
| Kodiak Island Borough Landfill | NA | AK | 228688 | Unknown | NA |
We also look at the first 5 rows of the AgSTAR Anaerobic Digester data. A few columns of note is the project name of the digester, its location, the project type, the type of animal on the farm, the year of operation, and the end use of the bio gas.
| project_name | state | project_type | animal_farm_type_s | year_operational | biogas_end_use_s |
|---|---|---|---|---|---|
| Cargill - Sandy River Farm Digester | AR | Farm Scale | Swine | 2008 | Flared Full-time |
| Butterfield RNG Digester | AZ | Farm Scale | Dairy | 2021 | Pipeline Gas |
| Caballero Dairy Farms Digester | AZ | Farm Scale | Dairy | 2022 | Pipeline Gas |
| Paloma Dairy Digester | AZ | Farm Scale | Dairy | NA | CNG |
| Stotz Southern Dairy Digester | AZ | Farm Scale | Dairy | 2011 | Electricity |
Project Breakdown
We now look at RNG project breakdown by operational year. Here we see how projects have ramped up over the years. We also see the increase in AgStar projects year by year.
In looking at RNG the operational project count by state, we see California leading the charge, followed by Pennsylvania and New York.
Project Type Breakdown
From a map perspective, we see a heat map of the number of landfill projects by state. Again California leads the charge in terms of landfills, followed by Michigan. We also notice Wyoming does not have any landfills that are allocated for landfill biogas projects.
From a map perspective, we see a heat map of the number of digester projects by state. Again California leads the charge in terms of landfills, followed by Wisconsin and New York. We also notice the absence of digester projects for states such as Nevada, New Mexico, and surprisingly Louisiana.
RNG Facility Locations
Landfill RNG Project Forecast
The outlook for Landfill RNG projects looks bright. We see over 30 Billion tons of waste candidate projects, as well as over 40 Billion tons of waste landfills that are either in the planning state or in currently being constructed.
Ag RNG Production Forecast
The outlook for AgStar projects looks bright as well. We see over 100,000 animals being used to produce biogas.
Bio Gas Generation Estimate/day (cubic ft)
Electricity Generation Estimate/year (KWH)
Demand
Project Count by End Use
Power Generation
Carbon Zero
Ag Emissions Reductions
Landfill Gas Emissions Reductions
Landfill Gas Emissions Avoidance
Summary
In looking at the data I believe the outlook for RNG is positive. Through the direction of the EPA we see states such as California, Michigan and New York leading the charge. We even see conservative states like Texas investing in RNG projects.
With more municipalities buying compressed natural gas vehicles we will continue to see demand rise. We also see electricity utilities replacing their current fossil fuel based natural gas with renewable natural gas.
As we embark on the road to Carbon zero, we see the use of RNG has an effect as data shows an increase in emission reduction and avoidance.